Sulfur nanoparticles synthesis and characterization from H2S gas, using novel biodegradable iron chelates in w/o microemulsion - Deshpande, Aniruddha S.; Khomane, Ramdas B.; Vaidya, Bhalchandra K.; Joshi, Renuka M.; Harle, Arti S.; Kulkarni, Bhaskar D. (Nanoscale Research Letters, 2008)
Sulfur nanoparticles were synthesized from hazardous H2S gas using novel biodegradable iron chelates in w/o microemulsion system. Fe3+-malic acid chelate (0.05 M aqueous solution) was studied in w/o microemulsion containing cyclohexane, Triton X-100 and n-hexanol as oil phase, surfactant, co-surfactant, respectively, for catalytic oxidation of H2S gas at ambient conditions of temperature, pressure, and neutral pH. The structural features of sulfur nanoparticles have been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM), energy dispersive spectroscopy (EDS), diffused reflectance infra-red Fourier transform technique, and BET surface area measurements. XRD analysis indicates the presence of alpha-sulfur. TEM analysis shows that the morphology of sulfur nanoparticles synthesized in w/o microemulsion system is nearly uniform in size (average particle size 10 nm) and narrow particle size distribution (in range of 5-15 nm) as compared to that in aqueous surfactant systems. The EDS analysis indicated high purity of sulfur (> 99%). Moreover, sulfur nanoparticles synthesized in w/o microemulsion system exhibit higher antimicrobial activity (against bacteria, yeast, and fungi) than that of colloidal sulfur.
Keywords: Antimicrobial activity; H2S gas; iron chelates; sulfur nanoparticles; w/o microemulsion

Use of response surface optimization for the production of biosurfactant from rhodococcus spp. MTCC 2574 - Mutalik, Snehal R.; Vaidya, Bhalchandra K.; Joshi, Renuka M.; Desai, Kiran M.; Nene, Sanjay N. (Bioresource Technology, 2008)
The production of biosurfactant from Rhodococcus spp. MTCC 2574 was effectively enhanced by response surface methodology (RSM). Rhodococcus spp. MTCC 2574 was selected through screening of seven different Rhodococcus strains. The preliminary screening experiments (one-factor at a time) suggested that carbon source: mannitol, nitrogen source: yeast extract and meat peptone and inducer: n-hexadecane are the critical medium components. The concentrations of these four media components were optimized by using central composite rotatable design (CCRD) of RSM. The adequately high R-2 value (0.947) and F score 19.11 indicated the statistical significance of the model. The optimum medium composition for biosurfactant production was found to contain mannitol (1.6 g/L), yeast extract (6.92 g/L), meat peptone (19.65 g/L), n-hexadecane (63.8 g/L). The crude biosurfactant was obtained from methyl tert-butyl ether extraction. The yield of biosurfactant before and after optimization was 3.2 g/L of and 10.9 g/L, respectively. Thus, RSM has increased the yield of biosurfactant to 3.4-fold. The crude biosurfactant decreased the surface tension of water from 72 mN/m to 30.8 mN/m (at 120 mg L-1) and achieved a critical, micelle concentration (CMC) value of 120 mg L-1. (C) 2008 Published by Elsevier Ltd.
Keywords: Biosurfactant; Medium optimization; Response surface methodology; Rhodococcus spp.

Enhanced production of amidase from Rhodococcus erythropolis MTCC 1526 by medium optimisation using a statistical experimental design - Vaidya, Bhalchandra K.; Mutalik, Snehal R.; Joshi, Renuka M.; Nene, Sanjay N.; Kulkarni, Bhaskar D. (Journal of Industrial Microbiology & Biotechnology, 2009)
In the present work, statistical experimental methodology was used to enhance the production of amidase from Rhodococcus erythropolis MTCC 1526. R. erythropolis MTCC 1526 was selected through screening of seven strains of Rhodococcus species. The Placket-Burman screening experiments suggested that sorbitol as carbon source, yeast extract and meat peptone as nitrogen sources, and acetamide as amidase inducer are the most influential media components. The concentrations of these four media components were optimised using a face-centred design of response surface methodology (RSM). The optimum medium composition for amidase production was found to contain sorbitol (5 g/L), yeast extract (4 g/L), meat peptone (2.5 g/L), and acetamide (12.25 mM). Amidase activities before and after optimisation were 157.85 units/g dry cells and 1,086.57 units/g dry cells, respectively. Thus, use of RSM increased production of amidase from R. erythropolis MTCC 1526 by 6.88-fold.
Keywords: Amidase; Medium optimisation; Plackett-Burman screening; Response surface methodology; Rhodococcus erythropolis

Media optimization for biosurfactant production by rhodococcus erythropolis MTCC 2794: artificial intelligence versus a statistical approach - Pal, Moumita P.; Vaidya, Bhalchandra K.; Desai, Kiran M.; Joshi, Renuka M.; Nene, Sanjay N.; Kulkarni, Bhaskar D. (Journal of Industrial Microbiology & Biotechnology, 2009)
This paper entails a comprehensive study on production of a biosurfactant from Rhodococcus erythropolis MTCC 2794. Two optimization techniques-(1) artificial neural network (ANN) coupled with genetic algorithm (GA) and (2) response surface methodology (RSM)-were used for media optimization in order to enhance the biosurfactant yield by Rhodococcus erythropolis MTCC 2794. ANN and RSM models were developed, incorporating the quantity of four medium components (sucrose, yeast extract, meat peptone, and toluene) as independent input variables and biosurfactant yield [calculated in terms of percent emulsification index (% EI24)] as output variable. ANN-GA and RSM were compared for their predictive and generalization ability using a separate data set of 16 experiments, for which the average quadratic errors were similar to 3 and similar to 6%, respectively. ANN-GA was found to be more accurate and consistent in predicting optimized conditions and maximum yield than RSM. For the ANN-GA model, the values of correlation coefficient and average quadratic error were similar to 0.99 and similar to 3%, respectively. It was also shown that ANN-based models could be used accurately for sensitivity analysis. ANN-GA-optimized media gave about a 3.5-fold enhancement in biosurfactant yield.
Keywords: Artificial neural network; Biosurfactant; Genetic algorithm; Media optimization; Response surface methodology; Rhodococcus